Single-RNA counting reveals alternative modes of gene expression in yeast

Seeing with an extra sense

Science foremost derives from our curiosity about the world. Can we make sense of the phenomena we see around us? Given that understanding, can we predict previously unimagined phenomena? How do things work? Can we use what we discover to invent new technologies? One class of questions that has mesmerized observers, dating at least to early cave paintings of hunters and their prey, surrounds the nature of the phenomenon we refer to as life. Over the centuries, scientists have found a broad array of surprisingly different techniques for observing, measuring, characterizing and explaining the living world. Microscopes provide a dazzling view of a previously unseen reality that tells us how living organisms are made up and how their components are organized and move. The tools of molecular science tell us the sequence and structure of the macromolecules that fill cells. The data explosion that has attended the development of a new generation of high-throughput tools for querying the living world demands that we have some way of accounting for those data that both provide intuition and make dangerous predictions with no after-the-fact parametric wiggle room. In this special issue of Current Biology, leading researchers explore how physical approaches have contributed to various fields of biology. Here, to introduce this special issue, I consider some of the ways in which viewing the living through a physical lens allows us to see things that might otherwise remain hidden.

All the sites we cannot see: Sources and mitigation of false negatives in RNA modification studies

RNA modifications are essential for human health - too much or too little of them leads to serious illnesses ranging from neurodevelopmental disorders to cancer. Technical advances in RNA modification sequencing are beginning to uncover the RNA targets of diverse RNA-modifying enzymes that are dysregulated in disease. However, the emerging transcriptome-wide maps of modified nucleosides installed by these enzymes should be considered as first drafts. In particular, a range of technical artefacts lead to false negatives - modified sites that are overlooked owing to technique-dependent, and often sequence-context-specific, 'blind spots'. In this Review, we discuss potential sources of false negatives in sequencing-based RNA modification maps, propose mitigation strategies and suggest guidelines for transparent reporting of sensitivity to detect modified sites in profiling studies. Important considerations for recognition and avoidance of false negatives include assessment and reporting of position-specific sequencing depth, identification of protocol-dependent RNA capture biases and applying controls for false negatives as well as for false positives. Despite their limitations, emerging maps of RNA modifications reveal exciting and largely uncharted potential for post-transcriptional control of all aspects of RNA function.

Chromatin endogenous cleavage provides a global view of yeast RNA polymerase II transcription kinetics

Chromatin immunoprecipitation (ChIP-seq) is the most common approach to observe global binding of proteins to DNA in vivo. The occupancy of transcription factors (TFs) from ChIP-seq agrees well with an alternative method, chromatin endogenous cleavage (ChEC-seq2). However, ChIP-seq and ChEC-seq2 reveal strikingly different patterns of enrichment of yeast RNA polymerase II. We hypothesized that this reflects distinct populations of RNAPII, some of which are captured by ChIP-seq and some of which are captured by ChEC-seq2. RNAPII association with enhancers and promoters - predicted from biochemical studies - is detected well by ChEC-seq2 but not by ChIP-seq. Enhancer/promoter bound RNAPII correlates with transcription levels and matches predicted occupancy based on published rates of enhancer recruitment, preinitiation assembly, initiation, elongation and termination. The occupancy from ChEC-seq2 allowed us to develop a stochastic model for global kinetics of RNAPII transcription which captured both the ChEC-seq2 data and changes upon chemical-genetic perturbations to transcription. Finally, RNAPII ChEC-seq2 and kinetic modeling suggests that a mutation in the Gcn4 transcription factor that blocks interaction with the NPC destabilizes promoter-associated RNAPII without altering its recruitment to the enhancer.

Single-cell new RNA sequencing reveals principles of transcription at the resolution of individual bursts

Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes.

Mitochondrial protein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence

A decline in mitochondrial function is a hallmark of aging and neurodegenerative diseases. It has been proposed that changes in mitochondrial morphology, including fragmentation of the tubular mitochondrial network, can lead to mitochondrial dysfunction, yet the mechanism of this loss of function is unclear. Most proteins contained within mitochondria are nuclear-encoded and must be properly targeted to the mitochondria. Here, we report that sustained mRNA localization and co-translational protein delivery leads to a heterogeneous protein distribution across fragmented mitochondria. We find that age-induced mitochondrial fragmentation drives a substantial increase in protein expression noise across fragments. Using a translational kinetic and molecular diffusion model, we find that protein expression noise is explained by the nature of stochastic compartmentalization and that co-translational protein delivery is the main contributor to increased heterogeneity. We observed that cells primarily reduce the variability in protein distribution by utilizing mitochondrial fission-fusion processes rather than relying on the mitophagy pathway. Furthermore, we are able to reduce the heterogeneity of the protein distribution by inhibiting co-translational protein targeting. This research lays the framework for a better understanding of the detrimental impact of mitochondrial fragmentation on the physiology of cells in aging and disease.

The DNA damage response and RNA Polymerase II regulator Def1 has posttranscriptional functions in the cytoplasm

Yeast Def1 mediates RNA polymerase II degradation and transcription elongation during stress. Def1 is predominantly cytoplasmic, and DNA damage signals cause its proteolytic processing, liberating its N-terminus to enter the nucleus. Cytoplasmic functions for this abundant protein have not been identified. Proximity-labeling (BioID) experiments indicate that Def1 binds to an array of proteins involved in posttranscriptional control and translation of mRNAs. Deleting DEF1 reduces both mRNA synthesis and decay rates, indicating transcript buffering in the mutant. Directly tethering Def1 to a reporter mRNA suppressed expression, suggesting that Def1 directly regulates mRNAs. Surprisingly, we found that Def1 interacts with polyribosomes, which requires its ubiquitin-binding domain located in its N-terminus. The binding of Def1 to ribosomes requires the ubiquitylation of eS7a (Rsp7A) in the small subunit by the Not4 protein in the Ccr4-Not complex. Not4 ubiquitylation of the ribosome regulates translation quality control and co-translational mRNA decay. The polyglutamine-rich unstructured C-terminus of Def1 is required for its interaction with decay and translation factors, suggesting that Def1 acts as a ubiquitin-dependent scaffold to link translation status to mRNA decay. Thus, we have identified a novel function for this transcription and DNA damage response factor in posttranscriptional regulation in the cytoplasm and establish Def1 as a master regulator of gene expression, functioning during transcription, mRNA decay, and translation.

Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference

Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.

How Transcription Factors Binding Stimulates Transcriptional Bursting

Transcription is a fundamental biological process of transferring genetic information which often occurs in stochastic bursts when periods of intense activity alternate with quiescent phases. Recent experiments identified strong correlations between the association of transcription factors (TFs) to gene promoters on DNA and transcriptional activity. However, the underlying molecular mechanisms of this phenomenon remain not well understood. Here, we present a theoretical framework that allowed us to investigate how binding dynamics of TF influences transcriptional bursting. Our minimal theoretical model incorporates the most relevant physical-chemical features, including TF exchange among multiple binding sites at gene promoters and TF association/dissociation dynamics. Using analytical calculations supported by Monte Carlo computer simulations, it is demonstrated that transcriptional bursting dynamics depends on the strength of TF binding and the number of binding sites. Stronger TF binding affinity prolongs burst duration but reduces variability, while an optimal number of binding sites maximizes transcriptional noise, facilitating cellular adaptation. Our theoretical method explains available experimental observations quantitatively, confirming the model's predictive accuracy. This study provides important insights into molecular mechanisms of gene expression and regulation, offering a new theoretical tool for understanding complex biological processes.

Transcriptional bursting: from fundamentals to novel insights

Transcription occurs as irregular bursts in a very wide range of systems, including numerous different species and many genes within these. In this review, we examine the underlying theories, discuss how these relate to experimental measurements, and explore some of the discrepancies that have emerged among various studies. Finally, we consider more recent works that integrate novel concepts, such as the involvement of biomolecular condensates in enhancer-promoter interactions and their effects on the dynamics of transcriptional bursting.

Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq

We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Polysome collapse and RNA condensation fluidize the cytoplasm

The cell interior is packed with macromolecules of mesoscale size, and this crowded milieu significantly influences cellular physiology. Cellular stress responses almost universally lead to inhibition of translation, resulting in polysome collapse and release of mRNA. The released mRNA molecules condense with RNA-binding proteins to form ribonucleoprotein (RNP) condensates known as processing bodies and stress granules. Here, we show that polysome collapse and condensation of RNA transiently fluidize the cytoplasm, and coarse-grained molecular dynamic simulations support this as a minimal mechanism for the observed biophysical changes. Increased mesoscale diffusivity correlates with the efficient formation of quality control bodies (Q-bodies), membraneless organelles that compartmentalize misfolded peptides during stress. Synthetic, light-induced RNA condensation also fluidizes the cytoplasm. Together, our study reveals a functional role for stress-induced translation inhibition and formation of RNP condensates in modulating the physical properties of the cytoplasm to enable efficient response of cells to stress conditions.

Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery

Non-genetic variations derived from expression noise at transcript or protein levels can result in cell-to-cell heterogeneity within an isogenic population. Although cells have developed strategies to reduce noise in some cellular functions, this heterogeneity can also facilitate varying levels of regulation and provide evolutionary benefits in specific environments. Despite several general characteristics of cellular noise having been revealed, the detailed molecular pathways underlying noise regulation remain elusive. Here, we established a dual-fluorescent reporter system in Saccharomyces cerevisiae and performed experimental evolution to search for mutations that increase expression noise. By analyzing evolved cells using bulk segregant analysis coupled with whole-genome sequencing, we identified the histone deacetylase Hos2 as a negative noise regulator. A hos2 mutant down-regulated multiple ribosomal protein genes and exhibited partially compromised protein translation, indicating that Hos2 may regulate protein expression noise by modulating the translation machinery. Treating cells with translation inhibitors or introducing mutations into several Hos2-regulated ribosomal protein genes-RPS9A, RPS28B and RPL42A-enhanced protein expression noise. Our study provides an effective strategy for identifying noise regulators and also sheds light on how cells regulate non-genetic variation through protein translation.

Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction

We introduce a biologically detailed, stochastic model of gene expression describing the multiple rate-limiting steps of transcription, nuclear pre-mRNA processing, nuclear mRNA export, cytoplasmic mRNA degradation and translation of mRNA into protein. The processes in sub-cellular compartments are described by an arbitrary number of processing stages, thus accounting for a significantly finer molecular description of gene expression than conventional models such as the telegraph, two-stage and three-stage models of gene expression. We use two distinct tools, queueing theory and model reduction using the slow-scale linear-noise approximation, to derive exact or approximate analytic expressions for the moments or distributions of nuclear mRNA, cytoplasmic mRNA and protein fluctuations, as well as lower bounds for their Fano factors in steady-state conditions. We use these to study the phase diagram of the stochastic model; in particular we derive parametric conditions determining three types of transitions in the properties of mRNA fluctuations: from sub-Poissonian to super-Poissonian noise, from high noise in the nucleus to high noise in the cytoplasm, and from a monotonic increase to a monotonic decrease of the Fano factor with the number of processing stages. In contrast, protein fluctuations are always super-Poissonian and show weak dependence on the number of mRNA processing stages. Our results delineate the region of parameter space where conventional models give qualitatively incorrect results and provide insight into how the number of processing stages, e.g. the number of rate-limiting steps in initiation, splicing and mRNA degradation, shape stochastic gene expression by modulation of molecular memory.

Gene activity fully predicts transcriptional bursting dynamics

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.

Unified bursting strategies in ectopic and endogenous even-skipped expression patterns

Transcription often occurs in bursts as gene promoters switch stochastically between active and inactive states. Enhancers can dictate transcriptional activity in animal development through the modulation of burst frequency, duration, or amplitude. Previous studies observed that different enhancers can achieve a wide range of transcriptional outputs through the same strategies of bursting control. For example, despite responding to different transcription factors, all even-skipped enhancers increase transcription by upregulating burst frequency and amplitude while burst duration remains largely constant. These shared bursting strategies suggest that a unified molecular mechanism constraints how enhancers modulate transcriptional output. Alternatively, different enhancers could have converged on the same bursting control strategy because of natural selection favoring one of these particular strategies. To distinguish between these two scenarios, we compared transcriptional bursting between endogenous and ectopic gene expression patterns. Because enhancers act under different regulatory inputs in ectopic patterns, dissimilar bursting control strategies between endogenous and ectopic patterns would suggest that enhancers adapted their bursting strategies to their trans-regulatory environment. Here, we generated ectopic even-skipped transcription patterns in fruit fly embryos and discovered that bursting strategies remain consistent in endogenous and ectopic even-skipped expression. These results provide evidence for a unified molecular mechanism shaping even-skipped bursting strategies and serve as a starting point to uncover the realm of strategies employed by other enhancers.

Solving stochastic gene-expression models using queueing theory: A tutorial review

Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods. Here, we provide a tutorial review of an alternative approach based on queueing theory that has rarely been used in the literature of gene expression. We discuss the interpretation of six types of infinite-server queues from the angle of stochastic single-cell biology and provide analytical expressions for the stationary and nonstationary distributions and/or moments of mRNA/protein numbers and bounds on the Fano factor. This approach may enable the solution of complex models that have hitherto evaded analytical solution.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

BACKGROUND

Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY

The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis.

CONCLUSIONS

According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

HIV-1 uncoating requires long double-stranded reverse transcription products

HIV-1 cores, which contain the viral genome and replication machinery, must disassemble (uncoat) during viral replication. However, the viral and host factors that trigger uncoating remain unidentified. Recent studies show that infectious cores enter the nucleus and uncoat near the site of integration. Here, we show that efficient uncoating of nuclear cores requires synthesis of a double-stranded DNA (dsDNA) genome >3.5 kb and that the efficiency of uncoating correlates with genome size. Core disruption by capsid inhibitors releases viral DNA, some of which integrates. However, most of the viral DNA is degraded, indicating that the intact core safeguards viral DNA. Atomic force microscopy and core content estimation reveal that synthesis of full-length genomic dsDNA induces substantial internal strain on the core to promote uncoating. We conclude that HIV-1 cores protect viral DNA from degradation by host factors and that synthesis of long double-stranded reverse transcription products is required to trigger efficient HIV-1 uncoating.

Yeast heterochromatin stably silences only weak regulatory elements by altering burst duration

Transcriptional silencing in Saccharomyces cerevisiae involves the generation of a chromatin state that stably represses transcription. Using multiple reporter assays, a diverse set of upstream activating sequence enhancers and core promoters were investigated for their susceptibility to silencing. We show that heterochromatin stably silences only weak and stress-induced regulatory elements but is unable to stably repress housekeeping gene regulatory elements, and the partial repression of these elements did not result in bistable expression states. Permutation analysis of enhancers and promoters indicates that both elements are targets of repression. Chromatin remodelers help specific regulatory elements to resist repression, most probably by altering nucleosome mobility and changing transcription burst duration. The strong enhancers/promoters can be repressed if silencer-bound Sir1 is increased. Together, our data suggest that the heterochromatic locus has been optimized to stably silence the weak mating-type gene regulatory elements but not strong housekeeping gene regulatory sequences.

Mitigating transcription noise via protein sharing in syncytial cells

Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.

The rise of single-cell transcriptomics in yeast

The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.

Implication of polymerase recycling for nascent transcript quantification by live cell imaging

Transcription enables the production of RNA from a DNA template. Due to the highly dynamic nature of transcription, live-cell imaging methods play a crucial role in measuring the kinetics of this process. For instance, transcriptional bursts have been visualized using fluorescent phage-coat proteins that associate tightly with messenger RNA (mRNA) stem loops formed on nascent transcripts. To convert the signal emanating from a transcription site into meaningful estimates of transcription dynamics, the influence of various parameters on the measured signal must be evaluated. Here, the effect of gene length on the intensity of the transcription site focus was analyzed. Intuitively, a longer gene can support a larger number of transcribing polymerases, thus leading to an increase in the measured signal. However, measurements of transcription induced by hyper-osmotic stress responsive promoters display independence from gene length. A mathematical model of the stress-induced transcription process suggests that the formation of gene loops that favor the recycling of polymerase from the terminator to the promoter can explain the observed behavior. One experimentally validated prediction from this model is that the amount of mRNA produced from a short gene should be higher than for a long one as the density of active polymerase on the short gene will be increased by polymerase recycling. Our data suggest that this recycling contributes significantly to the expression output from a gene and that polymerase recycling is modulated by the promoter identity and the cellular state.

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.

Single-Virion Analysis: A Method to Visualize HIV-1 Particle Content Using Fluorescence Microscopy

HIV-1 virions incorporate viral RNA, cellular RNAs, and proteins during the assembly process. Some of these components, such as the viral RNA genome and viral proteins, are essential for viral replication, whereas others, such as host innate immune proteins, can inhibit virus replication. Therefore, analyzing the virion content is an integral part of studying HIV-1 replication. Traditionally, virion contents have been examined using biochemical assays, which can provide information on the presence or absence of the molecule of interest but not its distribution in the virion population. Here, we describe a method, single-virion analysis, that directly examines the presence of molecules of interest in individual viral particles using fluorescence microscopy. Thus, this method can detect both the presence and the distribution of molecules of interest in the virion population. Single-virion analysis was first developed to study HIV-1 RNA genome packaging. In this assay, HIV-1 unspliced RNA is labeled with a fluorescently tagged RNA-binding protein (protein A) and some of the Gag proteins are labeled with a different fluorescent protein (protein B). Using fluorescence microscopy, HIV-1 particles can be identified by the fluorescent protein B signal and the presence of unspliced HIV-1 RNA can be identified by the fluorescent protein A signal. Therefore, the proportions of particles that contain unspliced RNA can be determined by the fraction of Gag particles that also have a colocalized RNA signal. By tagging the molecule of interest with fluorescent proteins, single-virion analysis can be easily adapted to study the incorporation of other viral or host cell molecules into particles. Indeed, this method has been adapted to examine the proportion of HIV-1 particles that contain APOBEC3 proteins and the fraction of particles that contain a modified Gag protein. Therefore, single-virion analysis is a flexible method to study the nucleic acid and protein content of HIV-1 particles.

RNA and Protein Detection by Single-Molecule Fluorescent in Situ Hybridization (smFISH) Combined with Immunofluorescence in the Budding Yeast S. cerevisiae

The inherent stochastic processes governing gene expression give rise to heterogeneity across individual cells, highlighting the importance of single-cell studies. The emergence of single-molecule fluorescent in situ hybridization (smFISH) enabled gene expression analysis at the single-cell level while including the spatial dimension through the visualization and quantification of mRNAs in intact fixed cells. By combining smFISH with immunofluorescence (IF), a comprehensive approach takes shape facilitating the study of mRNAs and proteins to correlate gene expression profiles to different cellular states. This chapter serves as a comprehensive guide to a smFISH-IF protocol optimized for gene expression analysis in the budding yeast S. cerevisiae. We utilize smFISH to visualize the mRNA localization pattern of the CLB2 cyclin over the course of the cell cycle inferred by alpha-tubulin IF.

Measuring Transcription Dynamics of Individual Genes Inside Living Cells

Transcription is a highly dynamic process, which, for many genes, occurs in stochastic bursts. Studying what regulates these stochastic bursts requires visualization and quantification of transcription dynamics in single living cells. Such measurements of bursting can be accomplished by labeling nascent transcripts of single genes fluorescently with the MS2 and PP7 RNA labeling techniques. Live-cell single-molecule microscopy of transcription in real time allows for the extraction of transcriptional bursting kinetics inside single cells. This chapter describes how to set up the MS2 or PP7 RNA labeling system of endogenous genes in both budding yeast (Saccharomyces cerevisiae) and mammalian cells (mouse embryonic stem cells). We include how to genetically engineer the cells with the MS2 and PP7 system, describe how to perform the live-microscopy experiments and discuss how to extract transcriptional bursting parameters of the genes of interest.

Combined 3D DNA FISH, Single-Molecule RNA FISH, and Immunofluorescence

Nuclear architecture is a potential regulator of gene expression in eukaryotic cells. Studies connecting nuclear architecture to gene expression are often population-averaged and do not report on the cell-level heterogeneity in genome organization and associated gene expression. In this report we present a simple way to combine fluorescence in situ hybridization (FISH)-based detection of DNA, with single-molecule RNA FISH (smFISH) and immunofluorescence (IF), while also preserving the three-dimensional (3D) nuclear architecture of a cell. Recently developed smFISH techniques enable the detection of individual RNA molecules; while using 3D DNA FISH, copy numbers and positions of genes inside the nucleus can be interrogated without interfering with 3D nuclear architecture. Our method to combine 3D DNA FISH with smFISH and IF enables a unique quantitative handle on the central dogma of molecular biology.

Imaging HIV-1 Nuclear Import, Uncoating, and Proviral Transcription

Live-cell imaging has become a powerful tool for dissecting the behavior of viral complexes during HIV-1 infection with high temporal and spatial resolution. Very few HIV-1 particles in a viral population are infectious and successfully complete replication (~1/50). Single-particle live-cell imaging enables the study of these rare infectious viral particles, which cannot be accomplished in biochemical assays that measure the average property of the entire viral population, most of which are not infectious. The timing and location of many events in the early stage of the HIV-1 life cycle, including nuclear import, uncoating, and integration, have only recently been elucidated. Live-cell imaging also provides a valuable approach to study interactions of viral and host factors in distinct cellular compartments and at specific stages of viral replication. Successful live-cell imaging experiments require careful consideration of the fluorescent labeling method used and avoid or minimize its potential impact on normal viral replication and produce misleading results. Ideally, it is beneficial to utilize multiple virus labeling strategies and compare the results to ensure that the virion labeling did not adversely influence the viral replication step that is under investigation. Another potential benefit of using different labeling strategies is that they can provide information about the state of the viral complexes. Here, we describe our methods that utilize multiple fluorescent protein labeling approaches to visualize and quantify important events in the HIV-1 life cycle, including docking HIV-1 particles with the nuclear envelope (NE) and their nuclear import, uncoating, and proviral transcription.

Single-Molecule Fluorescent In Situ Hybridization (smFISH) for RNA Detection in the Fungal Pathogen Candida albicans

Candida albicans is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of C. albicans to reversibly change morphology and to grow as yeast, pseudohyphae, or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help eradicate C. albicans infections.While numerous studies investigated gene expression changes occurring during C. albicans morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single-molecule fluorescent in situ hybridization (smFISH) protocol for C. albicans, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae, or hyphal cells. We quantified the expression of two mRNAs, a cell cycle-controlled mRNA (CLB2) and a transcription factor (EFG1), which show expression changes in the different morphological cell types and nutrient conditions. In this protocol, we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell segmentation, and mRNA spot analysis. Raw data is provided with the protocol to favor reproducibility. This approach could benefit the molecular characterization of C. albicans and other filamentous fungi, pathogenic or nonpathogenic.

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions. Here, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations, we show that this model enables accurate estimation of initiation and pause-escape rates. Furthermore, we show by simulation and analysis of real data that pause-escape is often strongly rate-limiting and that steric hindrance can dramatically reduce initiation rates. Our modeling framework is applicable to a variety of inference problems, and our software for estimation and simulation is freely available.

Rie1 and Sgn1 form an RNA-binding complex that enforces the meiotic entry cell fate decision

Budding yeast cells have the capacity to adopt few but distinct physiological states depending on environmental conditions. Vegetative cells proliferate rapidly by budding while spores can survive prolonged periods of nutrient deprivation and/or desiccation. Whether or not a yeast cell will enter meiosis and sporulate represents a critical decision that could be lethal if made in error. Most cell fate decisions, including those of yeast, are understood as being triggered by the activation of master transcription factors. However, mechanisms that enforce cell fates posttranscriptionally have been more difficult to attain. Here, we perform a forward genetic screen to determine RNA-binding proteins that affect meiotic entry at the posttranscriptional level. Our screen revealed several candidates with meiotic entry phenotypes, the most significant being RIE1, which encodes an RRM-containing protein. We demonstrate that Rie1 binds RNA, is associated with the translational machinery, and acts posttranscriptionally to enhance protein levels of the master transcription factor Ime1 in sporulation conditions. We also identified a physical binding partner of Rie1, Sgn1, which is another RRM-containing protein that plays a role in timely Ime1 expression. We demonstrate that these proteins act independently of cell size regulation pathways to promote meiotic entry. We propose a model explaining how constitutively expressed RNA-binding proteins, such as Rie1 and Sgn1, can act in cell fate decisions both as switch-like enforcers and as repressors of spurious cell fate activation.

Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks

Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner's Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.

Stochastic modeling of the mRNA life process: A generalized master equation

The mRNA life cycle is a complex biochemical process, involving transcription initiation, elongation, termination, splicing, and degradation. Each of these molecular events is multistep and can create a memory. The effect of this molecular memory on gene expression is not clear, although there are many related yet scattered experimental reports. To address this important issue, we develop a general theoretical framework formulated as a master equation in the sense of queue theory, which can reduce to multiple previously studied gene models in limiting cases. This framework allows us to interpret experimental observations, extract kinetic parameters from experimental data, and identify how the mRNA kinetics vary under regulatory influences. Notably, it allows us to evaluate the influences of elongation processes on mature RNA distribution; e.g., we find that the non-exponential elongation time can induce the bimodal mRNA expression and there is an optimal elongation noise intensity such that the mature RNA noise achieves the lowest level. In a word, our framework can not only provide insight into complex mRNA life processes but also bridge a dialogue between theoretical studies and experimental data.

Following the Birth, Life, and Death of mRNAs in Single Cells

Recent advances in single-molecule imaging of mRNAs in fixed and living cells have enabled the lives of mRNAs to be studied with unprecedented spatial and temporal detail. These approaches have moved beyond simply being able to observe specific events and have begun to allow an understanding of how regulation is coupled between steps in the mRNA life cycle. Additionally, these methodologies are now being applied in multicellular systems and animals to provide more nuanced insights into the physiological regulation of RNA metabolism.

Yeast Heterochromatin Only Stably Silences Weak Regulatory Elements by Altering Burst Duration

The interplay between nucleosomes and transcription factors leads to programs of gene expression. Transcriptional silencing involves the generation of a chromatin state that represses transcription and is faithfully propagated through DNA replication and cell division. Using multiple reporter assays, including directly visualizing transcription in single cells, we investigated a diverse set of UAS enhancers and core promoters for their susceptibility to heterochromatic gene silencing. These results show that heterochromatin only stably silences weak and stress induced regulatory elements but is unable to stably repress housekeeping gene regulatory elements and the partial repression did not result in bistable expression states. Permutation analysis of different UAS enhancers and core promoters indicate that both elements function together to determine the susceptibility of regulatory sequences to repression. Specific histone modifiers and chromatin remodellers function in an enhancer specific manner to aid these elements to resist repression suggesting that Sir proteins likely function in part by reducing nucleosome mobility. We also show that the strong housekeeping regulatory elements can be repressed if silencer bound Sir1 is increased, suggesting that Sir1 is a limiting component in silencing. Together, our data suggest that the heterochromatic locus has been optimized to stably silence the weak mating type gene regulatory elements but not strong housekeeping gene regulatory sequences which could help explain why these genes are often found at the boundaries of silenced domains.

Chromatinization Modulates Topoisomerase II Processivity

Type IIA topoisomerases are essential DNA processing enzymes that must robustly and reliably relax DNA torsional stress in vivo. While cellular processes constantly create different degrees of torsional stress, how this stress feeds back to control type IIA topoisomerase function remains obscure. Using a suite of single-molecule approaches, we examined the torsional impact on supercoiling relaxation of both naked DNA and chromatin by eukaryotic topoisomerase II (topo II). We observed that topo II was at least ~ 50-fold more processive on plectonemic DNA than previously estimated, capable of relaxing > 6000 turns. We further discovered that topo II could relax supercoiled DNA prior to plectoneme formation, but with a ~100-fold reduction in processivity; strikingly, the relaxation rate in this regime decreased with diminishing torsion in a manner consistent with the capture of transient DNA loops by topo II. Chromatinization preserved the high processivity of the enzyme under high torsional stress. Interestingly, topo II was still highly processive (~ 1000 turns) even under low torsional stress, consistent with the predisposition of chromatin to readily form DNA crossings. This work establishes that chromatin is a major stimulant of topo II function, capable of enhancing function even under low torsional stress.

Get out or die trying: Peptide- and protein-based endosomal escape of RNA therapeutics

RNA therapeutics offer great potential to transform the biomedical landscape, encompassing the treatment of hereditary conditions and the development of better vaccines. However, the delivery of RNAs into the cell is hampered, among others, by poor endosomal escape. This major hurdle is often tackled using special lipids, polymers, or protein-based delivery vectors. In this review, we will focus on the most prominent peptide- and protein-based endosomal escape strategies with focus on RNA drugs. We discuss cell penetrating peptides, which are still incorporated into novel transfection systems today to promote endosomal escape. However, direct evidence for enhanced endosomal escape by the action of such peptides is missing and their transfection efficiency, even in permissive cell culture conditions, is rather low. Endosomal escape by the help of pore forming proteins or phospholipases, on the other hand, allowed to generate more efficient transfection systems. These are, however, often hampered by considerable toxicity and immunogenicity. We conclude that the perfect enhancer of endosomal escape has yet to be devised. To increase the chances of success, any new transfection system should be tested under relevant conditions and guided by assays that allow direct quantification of endosomal escape.

Uncovering the effect of RNA polymerase steric interactions on gene expression noise: Analytical distributions of nascent and mature RNA numbers

The telegraph model is the standard model of stochastic gene expression, which can be solved exactly to obtain the distribution of mature RNA numbers per cell. A modification of this model also leads to an analytical distribution of nascent RNA numbers. These solutions are routinely used for the analysis of single-cell data, including the inference of transcriptional parameters. However, these models neglect important mechanistic features of transcription elongation, such as the stochastic movement of RNA polymerases and their steric (excluded-volume) interactions. Here we construct a model of gene expression describing promoter switching between inactive and active states, binding of RNA polymerases in the active state, their stochastic movement including steric interactions along the gene, and their unbinding leading to a mature transcript that subsequently decays. We derive the steady-state distributions of the nascent and mature RNA numbers in two important limiting cases: constitutive expression and slow promoter switching. We show that RNA fluctuations are suppressed by steric interactions between RNA polymerases, and that this suppression can in some instances even lead to sub-Poissonian fluctuations; these effects are most pronounced for nascent RNA and less prominent for mature RNA, since the latter is not a direct sensor of transcription. We find a relationship between the parameters of our microscopic mechanistic model and those of the standard models that ensures excellent consistency in their prediction of the first and second RNA number moments over vast regions of parameter space, encompassing slow, intermediate, and rapid promoter switching, provided the RNA number distributions are Poissonian or super-Poissonian. Furthermore, we identify the limitations of inference from mature RNA data, specifically showing that it cannot differentiate between highly distinct RNA polymerase traffic patterns on a gene.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

Bursting translation on single mRNAs in live cells

Stochasticity has emerged as a mechanism of gene regulation. Much of this so-called "noise" has been attributed to bursting transcription. Although bursting transcription has been studied extensively, the role of stochasticity in translation has not been fully investigated due to the lack of enabling imaging technology. In this study, we developed techniques to track single mRNAs and their translation in live cells for hours, allowing the measurement of previously uncharacterized translation dynamics. We applied genetic and pharmacological perturbations to control translation kinetics and found that, like transcription, translation is not a constitutive process but instead cycles between inactive and active states, or "bursts." However, unlike transcription, which is largely frequency-modulated, complex structures in the 5'-untranslated region alter burst amplitudes. Bursting frequency can be controlled through cap-proximal sequences and trans-acting factors such as eIF4F. We coupled single-molecule imaging with stochastic modeling to quantitatively determine the kinetic parameters of translational bursting.

Whole-mount smFISH allows combining RNA and protein quantification at cellular and subcellular resolution

Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.

Transcription activator WCC recruits deacetylase HDA3 to control transcription dynamics and bursting in Neurospora

RNA polymerase II initiates transcription either randomly or in bursts. We examined the light-dependent transcriptional activator White Collar Complex (WCC) of Neurospora to characterize the transcriptional dynamics of the strong vivid (vvd) promoter and the weaker frequency (frq) promoter. We show that WCC is not only an activator but also represses transcription by recruiting histone deacetylase 3 (HDA3). Our data suggest that bursts of frq transcription are governed by a long-lived refractory state established and maintained by WCC and HDA3 at the core promoter, whereas transcription of vvd is determined by WCC binding dynamics at an upstream activating sequence. Thus, in addition to stochastic binding of transcription factors, transcription factor-mediated repression may also influence transcriptional bursting.

Variability of the innate immune response is globally constrained by transcriptional bursting

Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.

Bayesian analysis dissects kinetic modulation during non-stationary gene expression

Labelling of nascent stem loops with fluorescent proteins has fostered the visualization of transcription in living cells. Quantitative analysis of recorded fluorescence traces can shed light on kinetic transcription parameters and regulatory mechanisms. However, existing methods typically focus on steady state dynamics. Here, we combine a stochastic process transcription model with a hierarchical Bayesian method to infer global as well locally shared parameters for groups of cells and recover unobserved quantities such as initiation times and polymerase loading of the gene. We apply our approach to the cyclic response of the yeast CUP1 locus to heavy metal stress. Within the previously described slow cycle of transcriptional activity on the scale of minutes, we discover fast time-modulated bursting on the scale of seconds. Model comparison suggests that slow oscillations of transcriptional output are regulated by the amplitude of the bursts. Several polymerases may initiate during a burst.

Transcriptional bursting: stochasticity in deterministic development

The transcription of DNA by RNA polymerase occurs as a discontinuous process described as transcriptional bursting. This bursting behavior is observed across species and has been quantified using various stochastic modeling approaches. There is a large body of evidence that suggests the bursts are actively modulated by transcriptional machinery and play a role in regulating developmental processes. Under a commonly used two-state model of transcription, various enhancer-, promoter- and chromatin microenvironment-associated features are found to differentially influence the size and frequency of bursting events - key parameters of the two-state model. Advancement of modeling and analysis tools has revealed that the simple two-state model and associated parameters may not sufficiently characterize the complex relationship between these features. The majority of experimental and modeling findings support the view of bursting as an evolutionarily conserved transcriptional control feature rather than an unintended byproduct of the transcription process. Stochastic transcriptional patterns contribute to enhanced cellular fitness and execution of proper development programs, which posit this mode of transcription as an important feature in developmental gene regulation. In this Review, we present compelling examples of the role of transcriptional bursting in development and explore the question of how stochastic transcription leads to deterministic organism development.

Rio1 downregulates centromeric RNA levels to promote the timely assembly of structurally fit kinetochores

Kinetochores assemble on centromeres via histone H3 variant CENP-A and low levels of centromere transcripts (cenRNAs). The latter are ensured by the downregulation of RNA polymerase II (RNAPII) activity, and cenRNA turnover by the nuclear exosome. Using S. cerevisiae, we now add protein kinase Rio1 to this scheme. Yeast cenRNAs are produced either as short (median lengths of 231 nt) or long (4458 nt) transcripts, in a 1:1 ratio. Rio1 limits their production by reducing RNAPII accessibility and promotes cenRNA degradation by the 5'-3'exoribonuclease Rat1. Rio1 similarly curtails the concentrations of noncoding pericenRNAs. These exist as short transcripts (225 nt) at levels that are minimally two orders of magnitude higher than the cenRNAs. In yeast depleted of Rio1, cen- and pericenRNAs accumulate, CEN nucleosomes and kinetochores misform, causing chromosome instability. The latter phenotypes are also observed with human cells lacking orthologue RioK1, suggesting that CEN regulation by Rio1/RioK1 is evolutionary conserved.